April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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294
13
章 時系列クラスタリング
ars = adjusted_rand_score(data_test[:,0], preds_test[0])
print("Adjusted Rand Index on Test Set:", ars)
テストセットの結果も
0.720
と素晴らしい。
Adjusted Rand Index on Test Set using HDBSCAN: 0.7200816245545564
13.7
時系列クラスタリングアルゴリズムの比較
ECG5000
データセットに関しては、
HDBSCAN
と
k-Shape
法の性能はほぼ同等で、
k
平均法はこれ
らよりかなり落ちる感じだった。しかし、
1
つの時系列データでの評価だけでこれら
3
つのアルゴリズ
ムを比較したのでは、あまり強固な結論を導くことはできない。
もっと大規模な実験を行って、これらの
3
つのクラスタリングアルゴリズムを比較してみよう。
まず、
UCR Time Series Classification Archive
からデータをダウンロードしたフォルダに入ってい
るすべてのディレクトリとファイルをロードして、繰り返し実験できるようにしよう。総計
85
のデータ
セットがある。
# Load the datasets
current_path = os.getcwd()
file = os.path.sep.join(['', 'datasets', 'ucr_time_series_data', ''])
mypath = current_path ...
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